Lecture 05 Demand Management and Forecasting

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1-1McGraw-Hill/Irwin 2009 The McGraw-Hill Companies, All Rights Reserved

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Demand Management

andForecasting


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Demand Management

Qualitative Forecasting

Methods

Simple & Weighted

Moving Average

Forecasts

Exponential Smoothing

Simple Linear Regression

Web-Based Forecasting

OBJECTIVES


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Demand Management

A

B(4) C(2)

D(2) E(1) D(3) F(2)

Dependent Demand:

Raw Materials,Component parts,

Sub-assemblies, etc.

Independent Demand:

Finished Goods


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Independent Demand:What a firm can do to manage it?

Can take an active role toinfluence demand

Can take a passive role andsimply respond to demand


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Types of Forecasts

Qualitative (Judgmental)

Quantitative

Time Series Analysis

Causal Relationships

Simulation


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Components of Demand

Average demand for a period

of time

Trend Seasonal element

Cyclical elements

Random variation

Autocorrelation


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Finding Components of Demand

1 2 3 4

x

x xx

xx

x xx

xx x x x

xxxxxx x x

xx

x x xx

xx

xx

x

xx

xx

xx

x

xx

xx

x

x

x

Year

Sales

Seasonal variation

Linear

Trend


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Qualitative Methods

Grass Roots

Market Research

Panel Consensus

Executive Judgment

Historical analogy

Delphi Method

Qualitative

Methods


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Delphi Method

l. Choose the experts to participaterepresenting a variety of knowledgeablepeople in different areas

2. Through a questionnaire (or E-mail), obtainforecasts (and any premises or

qualifications for the forecasts) from allparticipants3. Summarize the results and redistribute them

to the participants along with appropriatenew questions

4. Summarize again, refining forecasts andconditions, and again develop newquestions

5. Repeat Step 4 as necessary and distributethe final results to all participants


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Time Series Analysis

Time series forecasting modelstry to predict the future based on

past data

You can pick models based on:1. Time horizon to forecast

2. Data availability

3. Accuracy required

4. Size of forecasting budget

5. Availability of qualified

personnel


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Simple Moving Average Formula

F =A + A + A +...+A

nt t-1 t-2 t-3 t-n

The simple moving average model assumes anaverage is a good estimator of future behavior

The formula for the simple moving average is:

Ft = Forecast for the coming period

N = Number of periods to be averagedA t-1= Actual occurrence in the past period for up to n

periods

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Simple Moving Average Problem (1)

Week Demand

1 650

2 678

3 7204 785

5 859

6 920

7 850

8 7589 892

10 920

11 789

12 844

F = A + A + A +...+An

t t -1 t-2 t-3 t-n

Question: What are the 3-week and 6-week moving

average forecasts fordemand?

Assume you only have 3weeks and 6 weeks of

actual demand data for therespective forecasts

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Week Demand 3-Week 6-Week

1 650

2 678

3 720

4 785 682.67

5 859 727.676 920 788.00

7 850 854.67 768.67

8 758 876.33 802.00

9 892 842.67 815.33

10 920 833.33 844.00

11 789 856.67 866.50

12 844 867.00 854.83

F4=(650+678+720)/3

=682.67

F7=(650+678+720

+785+859+920)/6

=768.67

Calculating the moving averages gives us:

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500

600

700

800

900

1000

1 2 3 4 5 6 7 8 9 10 11 12

Week

Demand

Demand

3-Week

6-Week

Plotting the moving averages and comparing

them shows how the lines smooth out to reveal

the overall upward trend in this example

Note how the

3-Week issmoother than

the Demand,

and 6-Week is

even smoother

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Simple Moving Average Problem (2) Data

Week Demand

1 820

2 775

3 680

4 655

5 620

6 6007 575

Question: What is the3 week moving

average forecast

for this data?

Assume you only

have 3 weeks and

5 weeks of actual

demand data forthe respective

forecasts

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Simple Moving Average Problem (2) Solution

Week Demand 3-Week 5-Week

1 820

2 7753 680

4 655 758.33

5 620 703.33

6 600 651.67 710.00

7 575 625.00 666.00

F4=(820+775+680)/3

=758.33F6=(820+775+680

+655+620)/5

=710.00

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Weighted Moving Average Formula

F = w A + w A + w A + ...+ w At 1 t -1 2 t - 2 3 t -3 n t - n

w = 1ii=1

n

While the moving average formula implies an equalweight being placed on each value that is being averaged,

the weighted moving average permits an unequal

weighting on prior time periods

wt = weight given to time period t

occurrence (weights must add to one)

The formula for the moving average is:

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Weighted Moving Average Problem (1) Data

Weights:

t-1 .5t-2 .3

t-3 .2

Week Demand

1 650

2 678

3 720

4

Question: Given the weekly demand and weights, what isthe forecast for the 4th period or Week 4?

Note that the weights place more emphasis on the

most recent data, that is time period t-1

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Weighted Moving Average Problem (1) Solution

Week Demand Forecast1 650

2 678

3 720

4 693.4

F4 = 0.5(720)+0.3(678)+0.2(650)=693.4

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Weighted Moving Average Problem (2) Data

Weights:t-1 .7

t-2 .2

t-3 .1

Week Demand1 820

2 775

3 680

4 655

Question: Given the weekly demand information andweights, what is the weighted moving average forecast

of the 5th period or week?

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Weighted Moving Average Problem (2) Solution

Week Demand Forecast1 820

2 775

3 680

4 655

5 672

F5 = (0.1)(755)+(0.2)(680)+(0.7)(655)= 672

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Exponential Smoothing Model

Premise: The most recent observations mighthave the highest predictive value

Therefore, we should give more weight to the

more recent time periods when forecasting

Ft = Ft-1 + a(At-1 - Ft-1)

constantsmoothingAlpha

periodepast t timin theoccuranceActualA

periodpast time1inalueForecast vF

periodt timecomingfor thelueForcast vaF

:Where

1-t

1-t

t

a

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Exponential Smoothing Problem (1) Data

Week Demand1 820

2 775

3 680

4 6555 750

6 802

7 798

8 6899 775

10

Question: Given the

weekly demand

data, what are the

exponentialsmoothing

forecasts for

periods 2-10 using

a=0.10 and a=0.60?Assume F1=D1

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Week Demand 0.1 0.6

1 820 820.00 820.00

2 775 820.00 820.00

3 680 815.50 793.00

4 655 801.95 725.205 750 787.26 683.08

6 802 783.53 723.23

7 798 785.38 770.498 689 786.64 787.00

9 775 776.88 728.20

10 776.69 756.28

Answer: The respective alphas columns denote the forecast values. Note

that you can only forecast one time period into the future.

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Exponential Smoothing Problem (1) Plotting

500

600

700

800

900

1 2 3 4 5 6 7 8 9 10

Week

Demand Demand

0.1

0.6

Note how that the smaller alpha results in a smoother line in

this example

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Exponential Smoothing Problem (2) Data

Question: What are theexponential smoothing

forecasts for periods 2-5

using a =0.5?

Assume F1=D1

Week Demand

1 820

2 7753 680

4 655

5

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Exponential Smoothing Problem (2) Solution

Week Demand 0.5

1 820 820.00

2 775 820.00

3 680 797.50

4 655 738.75

5 696.88

F1=820+(0.5)(820-820)=820 F3=820+(0.5)(775-820)=797.75

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The MAD Statistic to Determine Forecasting Error

MAD =

A - F

n

t tt=1

n

1 MAD 0.8 standard deviation1 standard deviation 1.25 MAD

The ideal MAD is zero which would meanthere is no forecasting error

The larger the MAD, the less theaccurate the resulting model

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MAD Problem Data

Month Sales Forecast

1 220 n/a

2 250 255

3 210 205

4 300 320

5 325 315

Question: What is the MAD value given

the forecast values in the table below?

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MAD Problem Solution

MAD =A - F

n=

40

4= 10

t tt=1

n

Month Sales Forecast Abs Error

1 220 n/a

2 250 255 5

3 210 205 5

4 300 320 20

5 325 315 10

40

Note that by itself, the MAD

only lets us know the mean

error in a set of forecasts

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Tracking Signal Formula

The Tracking Signal or TS is a

measure that indicates whether theforecast average is keeping pace withany genuine upward or downwardchanges in demand.

Depending on the number of MADsselected, the TS can be used like aquality control chart indicating whenthe model is generating too mucherror in its forecasts.

The TS formula is:

TS =RSFE

MAD=

Running sum of forecast errors

Mean absolute deviation

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Simple Linear Regression Model

Yt = a + bx

0 1 2 3 4 5 x (Time)

YThe simple linear regression

model seeks to fit a line

through various data over

time

Is the linear regression model

a

Yt is the regressed forecast value or dependentvariable in the model, a is the intercept value of the theregression line, and b is similar to the slope of theregression line. However, since it is calculated with thevariability of the data in mind, its formulation is not asstraight forward as our usual notion of slope.

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Simple Linear Regression Formulas for Calculating a and b

a = y - b x

b = xy - n(y)(x)x - n(x2 2

)

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Simple Linear Regression Problem Data

Week Sales1 150

2 157

3 1624 166

5 177

Question: Given the data below, what is the simple linear

regression model that can be used to predict sales in futureweeks?

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Week Week*Week Sales Week*Sales

1 1 150 1502 4 157 314

3 9 162 486

4 16 166 664

5 25 177 885

3 55 162.4 2499

Average Sum Average Sum

b = xy - n(y)(x)x - n(x

= 2499 - 5(162.4)(3) =

a = y - bx = 162.4 - (6.3)(3) =

2 2

) ( )55 5 96310

6.3

143.5

Answer: First, using the linear regression formulas, we

can compute a and b

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Yt = 143.5 + 6.3x

180

Perio

d

135

140

145

150

155

160165

170

175

1 2 3 4 5

Sales Sales

Forecast

The resulting regression model

is:

Now if we plot the regression generated forecasts against the

actual sales we obtain the following chart:

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Web-Based Forecasting: CPFR

Collaborative Planning, Forecasting,

and Replenishment (CPFR) a Web-based tool used to coordinate demandforecasting, production and purchaseplanning, and inventory replenishmentbetween supply chain trading partners.

Used to integrate the multi-tier orn

-Tier supply chain, includingmanufacturers, distributors andretailers.

CPFRs objective is to exchangeselected internal information toprovide for a reliable, longer termfuture views of demand in the supplychain.

CPFR uses a cyclic and iterativeapproach to derive consensus

forecasts.

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Web-Based Forecasting:Steps in CPFR

1. Creation of a front-end partnershipagreement

2. Joint business planning

3. Development of demand forecasts

4. Sharing forecasts

5. Inventory replenishment

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End of Chapter 15

Lecture 05 Demand Management and Forecasting

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